ENH: Add option to use absolute value in pct_change

[Nick-Git2]

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Reproducible Example

import pandas as pd
test = pd.DataFrame()
test['testdata'] = [-68, 5144,10000]
test['pct_changes'] = test['testdata'].pct_change()
print (test['pct_changes'])
0          NaN
**1   -76.647059**
2     0.944012
Name: pct_changes, dtype: float64

Issue Description

correct result for row 1 should be 76.647059 and not -76.647059. It seems the current implementation of pct_change() lacks the abs() function and thus returns the wrong sign when the base is negative

Expected Behavior

percentage_change = (new_value - old_value) / abs(old_value)
(5144 - (-68)) / abs(-68) = 76.647059

Installed Versions

INSTALLED VERSIONS

commit : a671b5a
python : 3.11.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.133.1-microsoft-standard-WSL2
Version : #1 SMP Thu Oct 5 21:02:42 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.1.4
numpy : 1.24.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.17.2
pandas_datareader : 0.10.0
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : 2023.9.1
gcsfs : None
matplotlib : 3.8.0
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 13.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.11.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report. Taking the absolute value is common but not universal. It also gives counter-intuitive results - e.g. going from -20 to 20 is a smaller percent change than going from -10 to 20 (200% vs 300%).

In my opinion, one should not think of "percent change" as a measure how much something is getting more positive, but rather how much it's moving away from zero. Positive percent change means it's moving more away from zero, negative percent change means it's moving toward (and possibly crossing) 0. With this interpretation, not taking absolute value is correct.

Since it's fairly common to take the absolute value however, I wouldn't be opposed to adding an argument.

[rhshadrach]

I've reworked this issue as an enhancement request.

[paridhisharma24]

can I work on this

[rhshadrach]

cc @pandas-dev/pandas-core - any thoughts on adding an argument here?

[attack68]

isn't there an absolute function that can be applied to the result to return exactly the intent without the need for an argument?

I dont believe percent change should be absolute by default and it feels more pythonic, to me, to manipulate the outcome with existing functions than adding a keyword

[rhshadrach]

@attack68 - we need to apply abs to the denominator alone, not the entire result. This can currently be accomplished with .shift, .abs, a division, and a multiplication, but it's a bit messy.